---
language:
- en
license: mit
base_model: Abhiray/Sutra-Instruct-350M
tags:
- gguf
- llama.cpp
- quantized
- gpt2
- sutra
pipeline_tag: text-generation
library_name: gguf
---

# Sutra-Instruct-350M-GGUF

This repository contains the official GGUF quantizations of [**Sutra-Instruct-350M**](https://huggingface.co/Abhiray/Sutra-Instruct-350M), a 350-million parameter model trained entirely from scratch by Abhiray.

The **Sutra** (सूत्र) series is designed to be a high-speed, rule-based instruction model. These GGUF versions are optimized for ultra-low latency inference on consumer-grade hardware, including CPUs and mobile devices.

---

## 📁 Available Quantizations

| File Name | Quant Method | Size | Description |
| :--- | :--- | :--- | :--- |
| **sutra-fp16.gguf** | None (F16) | ~779 MB | Original weights. RECOMMENDED MOST Best for maximum quality and accuracy. |
| **sutra-Q8_0.gguf** | Q8_0 | ~417 MB | Standard 8-bit. Near-lossless; RECOMMENDED for most users. |
| **sutra-Q4_K_M.gguf** | Q4_K_M | ~258 MB | 4-bit Medium. Its for just testing not recommended much. |

---

## 🚀 Quick Start (Inference)

### 1. Using llama.cpp
Run the model directly from your terminal using `llama-cli`:

```bash
./llama-cli -m sutra-Q8_0.gguf -p "Instruction: What is the law of gravity?\n\nResponse:" -n 400 --temp 0.55 --repeat-penalty 1.2
```

### 2. Using Ollama (Plug-and-Play)
To use this with Ollama, create a file named Modelfile with the following content:

FROM ./sutra-Q8_0.gguf
TEMPLATE """Instruction:
{{ .Prompt }}

Response:
"""
PARAMETER stop "Instruction:"
PARAMETER stop "\nInstruction:"
PARAMETER temperature 0.55
PARAMETER repeat_penalty 1.2 
PARAMETER top_k 50
PARAMETER num_predict 512

Then, initialize and run the model:

ollama create sutra -f Modelfile
ollama run sutra

⚙️ Optimized Settings
To prevent the model from looping or hallucinating, we strongly recommend these inference parameters:

Temperature: 0.55

Repeat Penalty: 1.2

Top-K: 50

Max Tokens: 512